Category Archives: Internet of Things (IoT)

Quality 4.0 in Basic Terms (Interview)

On October 12th I dialed in to Quality Digest Live to chat with Dirk Dusharne, Editor-in-Chief of Quality Digest, about Quality 4.0 and my webinar on the topic which was held yesterday (October 16).

Check out my 13-minute interview here, starting at 14:05! It answers two questions:

  • What is Quality 4.0 – in really basic terms that are easy to remember?
  • How can we use these emerging technologies to support engagement and collaboration?

You can also read more about the topic here on the Intelex Community, or come to ASQ’s Quality 4.0 Summit in Dallas next month where I’ll be sharing more information along with other Quality 4.0 leaders like Jim Duarte of LJDUARTE and Associates and Dan Jacob of LNS Research.

Quality 4.0: Reveal Hidden Insights with Data Sci & Machine Learning (Webinar)

Quality Digest

What’s Quality 4.0, why is it important, and how can you use it to gain competitive advantage? Did you know you can benefit from Quality 4.0 even if you’re not a manufacturing organization? That’s right. I’ll tell you more next week.

Sign up for my 50-minute webinar at 2pm ET on Tuesday, October 16, 2018 — hosted by Dirk Dusharme and Mike Richman at Quality Digest. This won’t be your traditional “futures” talk to let you know about all of the exciting technology on the horizon… I’ve actually been doing and teaching data science, and applying machine learning to practical problems in quality improvement, for over a decade.

Come to this webinar if:

  1. You have a LOT of data and you don’t know where to begin
  2. You’re kind of behind… you still use paper and Excel and you’re hoping you don’t miss the opportunities here
  3. You’re a data scientist and you want to find out about quality and process improvement
  4. You’re a quality professional and you want to find out more about data science
  5. You’re a quality engineer and you want some professional preparation for what’s on the horizon
  6. You want to be sure you get on our Quality 4.0 mailing list to receive valuable information assets for the next couple years to help you identify and capture opportunities

Register Here! See you on Tuesday. If you can’t make it, we’ll also be at the ASQ Quality 4.0 Summit in Dallas next month sharing more information about the convergence of quality and Big Data.

Value Propositions for Quality 4.0

In previous articles, we introduced Quality 4.0, the pursuit of performance excellence as an integral part of an organization’s digital transformation. It’s one aspect of Industry 4.0 transformation towards intelligent automation: smart, hyperconnected(*) agents deployed in environments where humans and machines cooperate and leverage data to achieve shared goals.

Automation is a spectrum: an operator can specify a process that a computer or intelligent agent executes, the computer can make decisions for an operator to approve or adjust, or the computer can make and execute all decisions. Similarly, machine intelligence is a spectrum: an algorithm can provide advice, take action with approvals or adjustments, or take action on its own. We have to decide what value is generated when we introduce various degrees of intelligence and automation in our organizations.

How can Quality 4.0 help your organization? How can you improve the performance of your people, projects, products, and entire organizations by implementing technologies like artificial intelligence, machine learning, robotic process automation, and blockchain?

A value proposition is a statement that explains what benefits a product or activity will deliver. Quality 4.0 initiatives have these kinds of value propositions:

  1. Augment (or improve upon) human intelligence
  2. Increase the speed and quality of decision-making
  3. Improve transparency, traceability, and auditability
  4. Anticipate changes, reveal biases, and adapt to new circumstances and knowledge
  5. Evolve relationships and organizational boundaries to reveal opportunities for continuous improvement and new business models
  6. Learn how to learn; cultivate self-awareness and other-awareness as a skill

Quality 4.0 initiatives add intelligence to monitoring and managing operations – for example, predictive maintenance can help you anticipate equipment failures and proactively reduce downtime. They can help you assess supply chain risk on an ongoing basis, or help you decide whether to take corrective action. They can also improve help you improve cybersecurity: documenting and benchmarking processes can provide a basis for detecting anomalies, and understanding expected performance can help you detect potential attacks.


(*) Hyperconnected = (nearly) always on, (nearly) always accessible.

Blockchain and Quality

Quality is all about satisfying stated and implied needs –now, or in the future. When we envision and design high-quality products and services for the future, that’s innovation. One of the most hyped innovations of 2017 was blockchain, which has the potential to transform business models and the way quality is managed. The purpose of this article is to explain this relationship in a simple way.

Blockchain is the innovative technology supporting the Bitcoin cryptocurrency. Bitcoin gained tremendous traction in 2017, starting at just over $1,000 in January and reaching nearly $20,000 by the end of the year.  It increased in value so much over this time that it’s been compared to the Dutch tulip market bubble of the 1630s.  After tulips were imported into Holland from Turkey, an alteration to the solid colors of the tulips caused the appearance of “flames” on the petals. This made people believe that the tulip bulbs held extreme value, and so many people traded their land and their savings to invest in what they felt was a “sure thing” – to lose everything not long after, when the market corrected itself.

Bitcoin (USD) prices, 1/1/17-12/13/17. Generated using https://www.coindesk.com/price/.

Bitcoin (USD) prices, 1/1/17-12/13/17. Generated using https://www.coindesk.com/price/.

The blockchain technology that supports Bitcoin is, at its core, a database. It’s a special kind of database, but no more magical, really – and easier to contextualize if you think about innovations in database technology over the past two decades.

Databases can be roughly classified into these categories:

  • Relational databases (Oracle, MySQL, PostgreSQL, Sybase): When you can organize your data in terms of tables, fields, and relationships between those entities, a relational database is often appropriate. For example, your customer data might be kept in the “people” table with fields like address, state, or gender. Each record in the people table might have a type – employee, partner, or customer. Although records can be changed, it’s easy to accidentally input bad data, and it’s also easy to accidentally generate duplicate records. Scaling a relational database can also be rather tricky.
  • Non-relational (NoSQL) databases (MongoDB, Cassandra, Redis): If most of your data comes in large blobs and you don’t want to split it up into fields and tables, these databases are useful. MongoDB is great for collections of documents, such as web pages, log data, or tweets. Cassandra works well for analytics applications. Sensor data and other data types that change frequently or need to be held in active memory (for example, in key-value stores) are handled well by databases like Redis. NoSQL databases are easier to scale than relational databases.
  • Other databases and data stores with special properties: Some databases are so unique they don’t feel or act like databases. Solr, for example, is traditionally used when you have to provide search functionality over a store of documents. Hadoop is a distributed file system, so it functions somewhat like a database even though it’s not one. Graph databases are designed for data stores where the relationships are the most important aspect, so they are gaining popularity for social networks. Large, institutional science projects often store their data in special binary files that have distinct formats, can be queried like databases, and in many ways act like databases – but they are not technically databases.

 

What Distinguishes Blockchain-based Databases from Ordinary Databases?

First, the blockchain is designed to handle transactions – it’s a digital ledger. So it’s not surprising that its first “successful” use cases are in the realm of cryptocurrency, where people engage in transactions with one another to exchange something of value.

Next, this database is immutable, meaning you can’t go back and change earlier records. Every time a new transaction occurs, a cryptographically sealed “snapshot” is taken of the entire database. When I first heard this, I was worried: so that means if we accidentally enter something incorrect into the database, it can never be changed, right? And its presence is memorialized forever? The answer to this question is: sort of. Thanks to “smart contracts”, we shouldn’t ever be in the situation where bad data gets entered into our blockchain-based system, because incoming data will be checked (by multiple agents) against the smart contract — and only allowed to join the blockchain database if it meets all the quality requirements specified by the contract. It’s like a fancy way to implement validation rules – with the added benefit of being totally traceable. Imagine how nice it would be to trace all the steps in the process that brought the fresh fruit into your kitchen – or any other product you use — just because all transactions in the production process were logged into a “supply blockchain.”

A blockchain database is also decentralized and distributed — you don’t just “buy a blockchain database” and install it at your company. Databases can be centralized, decentralized, or distributed. Most business databases in the past were centralized: there was one instance installed, and a database administrator (or team of them) ensured the performance and security of the database while everyone in the organization created and used applications that interacted with the data. Today, these databases are more commonly distributed: there’s not just one instance, but several – there is no central storage, but there may be storage on many computers, or over a network of connected computers (or “in the cloud”). 

Decentralized systems have many advantages – for example, nodes can join or leave the network at will. For example, you can create a web site or take it off the internet whenever you want, if you own and control it. In decentralized systems, there is no single point of control. If a business wants to implement blockchain but also wants to control all the nodes, that should be a big red flag. By its nature, blockchain is decentralized just like the internet itself.

Finally, blockchain is transparent. Any of the participants who own nodes can see all the transactions — so there should be fewer opportunities for fraud. This doesn’t mean that there isn’t opportunity for danger, though.

 

Why is Blockchain Potentially Useful for Quality Assurance?

In addition to enhancing provenance and traceability, one of the biggest envisioned applications of blockchain databases is to support machine to machine transactions. As intelligent agents grow in complexity and are trusted to handle more tasks, and as the Internet of Things (IoT) expands, there needs to be a high-quality record of how those objects and agents interact with other objects and agents – and with humans. Blockchain could also be used to support new business models like decentralized energy markets, where you can consume energy from the local power plant, but also potentially generate your own and contribute the excess energy to your local community for a fee. It could potentially transform middleware as well, which is software that allows different software systems to communicate with one another. (A long time ago, someone told me that it’s like “email for applications” – they can send messages to one another so they know how to react, for example, when a company receives an order and several systems need to be alerted that the order has arrived.)

In principle, transactions logged to a blockchain make it impossible to defraud participants in the process, and impossible to manipulate records after they are recorded. They are self-auditing and fully traceable. Blockchain won’t make quality assurance, tracking, or auditing EASY, but you should expect it to make the business landscape different – new business models will be possible, and it will be possible to entrust intelligent agents with more tasks.  

Blockchain can help us ensure that stated and implied needs are met, and do it in such a way that the integrity of our data is assured simply by its presence. But we’re not there yet. Developers still need to implement simple, demonstrable use cases to make it easier for managers and executives to map these technologies onto specific business needs. In addition, blockchain is slow compared to relational database systems, so this needs to be addressed as well before widespread adoption.

 

Read more in our December 2017 SQP article.

Quality 4.0 and Digital Transformation

The fourth industrial revolution is characterized by intelligence: smart, hyperconnected agents deployed in environments where humans and machines cooperate to achieved shared goals — and using data to generate value. Quality 4.0 is the name we give to the pursuit of performance excellence in the midst of technological progress, which are sometimes referred to as digital transformation.

The characteristics of Quality 4.0 were first described in the 2015 American Society for Quality (ASQ) Future of Quality Report. This study aimed to uncover the key issues related to quality that could be expected to evolve over the next 5 to 10 years. In general, the analysts expected that the new reality would focus not so much on individual interests, but on the health and viability of the entire industrial ecosystem.

Some of the insights from the 2015 ASQ Future of Quality Report were:

  • A shifting emphasis from efficiency and effectiveness, to continuous learning and adaptability
  • Shifting seams and transitions (boundaries within and between organizations, and how information is shared between the different areas)
  • Supply chain omniscience (being able to assess the status of any element of a global supply chain in real time)
  • Managing data over the lifetime of the data rather than the organization collecting it

The World Economic Forum (WEF) has also been keenly interested in these changes for the past decade. In 2015, they launched a Digital Transformation Initiative (DTI) to coordinate research to help anticipate the impacts of these changes on business and society. They recognize that we’ve been actively experiencing digital transformation since the emergence of digital computing in the 1950’s:

 

Because the cost of enabling technologies has decreased so much over the past decade, it’s now possible for organizations to begin making them part of their digital strategy. In general, digital transformation reveals that the nature of “organization” is changing, and the nature of “customer” is changing as well. Organizations will no longer be defined solely by their employees and business partners, but also by the customers who participate – without even explicitly being aware of their integral involvement — in ongoing dialogues that shape the evolution of product lines and new services.

New business models will not necessarily rely on ownership, consumption, or centralized production of products or provision of services. The value-based approach will accentuate the importance of trust, transparency, and security, and new technologies (like blockchain) will help us implement and deploy systems to support those changes.

 

What is Quality 4.0?

Image Credit: Doug Buckley of http://hyperactive.to

My first post of the year addresses an idea that’s just starting to gain traction – one you’ll hear a lot more about from me in 2018: Quality 4.0.  It’s not a fad or trend, but a reminder that the business environment is changing, and that performance excellence in the future will depend on how well you adapt, change, and transform in response. Although we started building community around this concept at the ASQ Quality 4.0 Summit on Disruption, Innovation, and Change, held in November 2017 in Dallas, the truly revolutionary work is yet to come.

The term “Quality 4.0” comes from “Industry 4.0” – referring to the “fourth industrial revolution” – originally addressed at the Hannover (Germany) Fair in 2011. That meeting emphasized the increasing intelligence and interconnectedness in “smart” manufacturing systems and reflected on the newest technological innovations in historical context.

In the first industrial revolution (late 1700’s), steam and water power made it possible for production facilities to scale up and expanded the potential locations for production. By the late 1800’s, the discovery of electricity and development of associated infrastructure enabled the development of machines for mass production. In the US, the expansion of railways made it easier to obtain supplies and deliver finished goods. The availability of power also sparked a renaissance in computing, and digital computing emerged from its analog ancestor. The third industrial revolution came at the end of the 1960’s, with the invention of the Programmable Logic Controller (PLC). This made it possible to automate processes like filling and reloading tanks of liquids, turning engines on and off, and controlling sequences of events based on changing environmental conditions.

Although the growth and expansion of the internet accelerated innovation in the late 1990’s and 2000’s, we are just now poised for another industrial revolution. What’s changing?

  • Production & Availability of Information: More information is available because people and devices are producing it at greater rates than ever before. Falling costs of enabling technologies like sensors and actuators are catalyzing innovation in these areas.
  • Connectivity: In many cases, and from many locations, that information is instantly accessible over the internet. Improved network infrastructure is expanding the extent of connectivity, making it more widely available and more robust. (And unlike the 80’s and 90’s, there are far fewer communications protocols that are commonly encountered so it’s a lot easier to get one device to talk to another device on your network.)
  • Intelligent Processing: Affordable computing capabilities (and computing power!) are available to process that information so it can be incorporated into decision making. High-performance software libraries for advanced processing and visualization of data are easy to find, and easy to use. (In the past, we had to write our own… now we can use open-source solutions that are battle tested.
  • New Modes of Interaction: The way in which we can acquire and interact with information are also changing, in particular through new interfaces like Augmented Reality (AR) and Virtual Reality (VR), which expand possibilities for training and navigating a hybrid physical-digital environment with greater ease.
  • New Modes of Production: 3D printing, nanotechnology, and gene editing (CRISPR) are poised to change the nature and means of production in several industries. Technologies for enhancing human performance (e.g. exoskeletons, brain-computer interfaces, and even autonomous vehicles) will also open up new mechanisms for innovation in production. (Roco & Bainbridge (2002) describe many of these, and their prescience is remarkable.) New technologies like blockchain have the potential to change the nature of production as well, by challenging ingrained perceptions of trust, control, consensus, and value.

If the first industrial revolution was characterized by steam-powered machines, the second was characterized by electricity and assembly lines. Innovations in computing and industrial automation defined the third industrial revolution.  The fourth industrial revolution is one of intelligence: smart, hyperconnected cyber-physical systems in environments where humans and machines cooperate to achieved shared goals, and use data to generate value.

These enabling technologies originate in the physical, digital, and biological domains, and include the following:

  • Information
    • Affordable Sensors and Actuators
    • Big Data infrastructure (e.g. MapReduce, Hadoop, NoSQL databases)
  • Connectivity
    • 5G Networks
    • IPv6 Addresses (which expand the number of devices that can be put online)
    • Internet of Things (IoT)
    • Cloud Computing
  • Processing
    • Predictive Analytics
    • Artificial Intelligence
    • Machine Learning (incl. Deep Learning)
    • Data Science
  • Interaction
    • Augmented Reality (AR)
    • Mixed Reality (MR)
    • Virtual Reality (VR)
    • Diminished Reality (DR)
  • Construction
    • 3D Printing
    • Additive Manufacturing
    • Smart Materials
    • Nanotechnology
    • Gene Editing
    • Automated (Software) Code Generation
    • Robotic Process Automation (RPA)
    • Blockchain

Today’s quality profession was born during the middle of the second industrial revolution, when methods were needed to ensure that assembly lines ran smoothly – that they produced artifacts to specifications, that the workers knew how to engage in the process, and that costs were controlled. As industrial production matured, those methods grew to encompass the design of processes which were built to produce to specifications. In the 1980’s and 1990’s, organizations in the US started to recognize the importance of human capabilities and active engagement in quality as essential, and TQM, Lean, and Six Sigma gained in popularity. 

How will these methods evolve in an adaptive, intelligent environment? The question is largely still open, and that’s the essence of Quality 4.0.

Roco, M. C., & Bainbridge, W. S. (2002). Converging technologies for improving human performance: Integrating from the nanoscale. Journal of nanoparticle research4(4), 281-295. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.465.7221&rep=rep1&type=pdf)

Voice of the Customer (VOC) in the Internet of Things (IoT)

Image Credit: Doug Buckley of http://hyperactive.to

Image Credit: Doug Buckley of http://hyperactive.to

In February, I speculated about how our notion of “Voice of the Customer” (VoC) might change, since between 2016 and 2020 we are poised to witness the Internet of Things (IoT) as it grows from 6.4 billion to over 20 billion entities. The IoT will require us to re-think fundamental questions about how our interests as customer and stakeholders are represented. In particular,

  • What will the world look (and feel) like when everything you interact with has a “voice”?
  • How will the “Voice of the Customer” be heard when all of that customer’s stuff ALSO has a voice?
  • Will your stuff have “agency” — that is, the right to represent your needs and interests to other products and services?

Companies are also starting to envision how their strategies will morph in response to the new capabilities offered by the IoT. Starbucks CTO Gerri Martin-Flickenger, for example, shares her feelings in GeekWire, 3/24/2016:

“Imagine you’re on a road trip, diving across the country, and you pull into a Starbucks drive-through that you’ve never been to before,” she said at the Starbucks annual shareholder’s meeting Wednesday in Seattle. “We detect you’re a loyal customer and you buy about the same thing every day, at about the same time. So as you pull up to the order screen, we show you your order, and the barista welcomes you by name.”

“Does that sound crazy?” she asked. “No, actually, not really. In the coming months and years you will see us continue to deliver on a basic aspiration: to deliver technology that enhances the human connection.”

IoT to enhance the human connection? Sounds great, right? But hold on… that’s not what she’s talking about. She wants to enhance the feeling of connection between individuals and a companynothing different than cultivating customer loyalty.

Her scenario is actually pretty appealing: I can imagine pulling up to a Starbuck’s drive-through and having everything disappear from the screen except for maybe 2 or 3 choices of things I’ve had before, and 1 or 2 choices for things I might be interested in. The company could actually work with me to help alleviate my sensory overload problems, reducing the stress I experience when presented with a hundred-item menu, and improving my user experience. IoT can help them hear my voice  as a customer, and adapt to my preferences, but it won’t make them genuinely care about me any more than they already do not.

When I first read this article, I thought it would give me insight into a question I’ve had for a while now… but the question is still substantially unanswered: How can IoT facilitate capturing and responding to VoC in a way that really does cultivate human connection? John Hagel and John Seely Brown, in my opinion, are a little closer to the target:

[Examples] highlight a paradox inherent in connected devices and the Internet of Things: although technology aims to weave data streams without human intervention, its deeper value comes from connecting people. By offloading data capture and information transfer to the background, devices and applications can actually improve human relationships. Practitioners can use technology to get technology out of the way—to move data and information flows to the side and enable better human interaction…